Convolutional deep learning analysis of temporal cardiac images

ABSTRACT

A convolutional neural cardiac diagnostic system employs an echocardiogram diagnostic controller for controlling a diagnosis of an echocardiogram including a temporal sequence of echocardiac cycles. The echocardiogram diagnostic controller includes a diagnostic periodic volume generator generating an echocardiogram diagnostic volume including a periodic stacking of the temporal sequence of echocardiac cycles, and further includes a diagnostic convolutional neural network classifying the echocardiogram as either a normal echocardiogram or an abnormal echocardiogram based on a convolutional neural analysis of the echocardiogram diagnostic volume. The diagnostic periodic volume generator may further generate an electrocardiogram diagnostic volume including a periodic stacking of the temporal sequence of electrocardiac waves, and the convolutional neural network may classify the echocardiogram as either a normal echocardiogram or an abnormal echocardiogram based on a convolutional neural analysis of both the echocardiogram diagnostic volume and the electrocardiogram diagnostic volume.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. ProvisionalApplication No. 62/508,087, filed May 18, 2017. These applications arehereby icorporated by reference herein.

TECHNICAL FIELD

The present disclosure generally relates to image processing. Morespecifically, but not exclusively, the present disclosure relates toclassifying a cardiac image as illustrating normal cardiac functions orabnormal cardiac functions.

BACKGROUND

Many medical imaging modalities (e.g., ultrasound US, magnetic resonanceimaging MRI, CT, positron emission tomography PET, etc.) providetemporal data describing functions of various body organs. One of themain application of the temporal functional imaging is the diagnosis andmonitoring of heart disease. Because the heart is in constant periodicmotion, the temporal imaging is extensively used to characterize cardiacfunction by analysis of the cardiac deformation.

Echocardiography (echo) as known in the art of the present disclosure isone of the most popular techniques used to capture the temporal data ofa beating heart. Echo has several advantages over other imagingmodalities that include low cost and portability. Echo real-time imagingand does not use any ionizing radiation.

There are two (2) different acquisition modes, the most widely utilizedtwo-dimensional (2D) mode and a less popular three-dimensional mode (3D)mode.

For 2D echo, an ultrasound transducer is positioned close to the sternumand images in 2D planes intersecting the heart are acquired at 50 to 100per second frame rate. These movies (temporal sequence of planarechocardiac images) are visualized live for the sonographer and can besaved and sent for later interpretation/diagnosis (e.g. PACS). 2D echorequires an acquisition of several different planes going through theheart to cover the entire volume of the myocardium.

For 3D echo, a more sophisticated transducer is used and temporalsequence of volumetric echocardiac images of beating heart are acquired.

An electrocardiogram (ECG) as known in the art of the present disclosureincreases an ability to detect abnormal cardiovascular conditions (e.g.,cardiomyopathies) that may lead to sudden cardiac arrest. The result ofthe ECG is a waveform that indicates the electrical activity of theheart during the heart cycle, and an ECG is simultaneously performedwith an echo to enhance the cardiac diagnosis.

One major application of Echo is the detection and characterization ofcardiovascular disease (CVD). The disease may be a result of occlusionin one or more coronary arteries which results in reduced contractilityof one or more of the segments of the heart. In clinical applications ofecho, the abnormalities in cardiac wall motion are detected based ontemporal echo images and quantified. In the current practice, thisquantification is done by subjective visual examination of the temporalimages and detection of cardiac wall motion and thickening abnormalitiesper myocardial segment. The interpretation of the Echo may be doneeither during the examination as the images are visualized real-time orpost examination at the reading console (e.g. PACS). There are manyother types of cardiac diseases that come from abnormalities in cardiacfunction either electrical or mechanical in nature. The common dominatorof those diseases if that they manifest in either the cardiac structureor/and in function (electrical/mechanical).

There is a substantial research effort done into modelling of cardiacdeformation as evidenced by echo images. Majority of those efforts arebased on image analysis. For example, detection of endocardial wall maybe utilized and then quantified. Also, segmentation, speckle tracking,non-rigid registration approaches may be utilized to automatically trackthe cardiac motion and determine the motion abnormalities. However, allof those approaches suffer from a problem of severe noise in ultrasoundimages which prevents the robust implementation of these algorithms.

A different approach to this problem is to use a different dataacquisition model involving a Doppler acquisition of ultrasound in whichmotion of tissues can be quantified. For this approach however, themotion can only be quantified in beam direction and results aredependent on the signal-to-noise ratio.

SUMMARY

One of the major problems of the aforementioned Echo procedures is thediagnosis of CVD based on motion of the cardiac wall is done in acompletely subjective manner. An echocardiographer eyeballs the temporalviews and based on those views determines which segments exhibit motionabnormalities indicative of a reduced cardiac fiber contractility due toCVD.

The visual assessment that is used today is highly dependent onexperience and training of an echocardiographer. It follows thatinter-observer and intra-observer variability is significant. The otherdifficulty with interpretation of Echo is that it requires highlytrained professionals needed for interpretation of echo images. If theyare not promptly available or not available all the utility of Echo issubstantially reduced for instant diagnosis.

Moreover, as previously stated, echo examinations are typicallyaccompanied by the acquisition of ECG waveforms. However, the echo andthe ECG are interpreted separately reducing the synergy of these tests.

To improve upon detection and characterization of cardiovascular disease(CVD) via acquisition of echo cardiac images, the present disclosureprovides systems, devices, controllers and methods for standardizing aclassification/quantification of abnormal cardiac conditions (e.g.,heart wall motion abnormalities) evidenced by echo cardiac images whichmay be combined with electrocardiograms to thereby standardize adiagnosis of CVD using echo.

Generally, the present disclosure is premised on application of a deepconvolutional neural network to an echocardiogram based on a modellingof temporal changes in the echocardiogram.

One embodiment of the present disclosure is a convolutional neuralcardiac diagnostic system including one or more of the following: anultrasound device for generating echocardiogram data and anechocardiogram controller for controlling a generation of anechocardiogram derived from the echocardiogram data. The echocardiogramincludes a temporal sequence of echocardiac cycles.

The convolutional neural cardiac diagnostic system further includes acardiac diagnostic controller for controlling a diagnosis of theechocardiogram. To this end, the cardiac diagnostic controller includesa periodic volume generator for generating an echocardiogram diagnosticvolume including a periodic stacking of the temporal sequence ofechocardiac cycles and further includes a diagnostic convolutionalneural network for classifying(quantifying) the echocardiogram as one ofa normal echocardiogram or an abnormal echocardiogram based on aconvolutional neural analysis of the echocardiogram diagnostic volume.

A second embodiment of the present disclosure is the convolutionalneural diagnostic echo system further including a lead system forgenerating electrocardiogram data, and an electrocardiogram controllerfor controlling a generation of an electrocardiogram derived from theelectrocardiogram data. The electrocardiogram includes a temporalsequence of electrocardiogram waves.

The periodic volume generator further generates an electrocardiogramdiagnostic volume including a periodic stacking of the temporal sequenceof electrocardiogram waves, and the diagnostic convolutional neuralnetwork classifies(quantifies) the echocardiogram as one of the normalechocardiogram or the abnormal echocardiogram based on a convolutionalneural analysis of both the echocardiogram diagnostic volume and theelectrocardiogram diagnostic volume.

A third embodiment of the present disclosure is a convolutional neuralcardiac diagnostic method including one or more of the following anultrasound device generating echocardiogram data, and an echocardiogramcontroller controlling a generation of an echocardiogram derived fromthe echocardiogram data. The echocardiogram includes a temporal sequenceof echocardiac cycles.

The convolutional neural cardiac diagnostic method further includes acardiac diagnostic controller controlling a diagnosis of theechocardiogram by generating an echocardiogram diagnostic volumeincluding a periodic stacking of the temporal sequence of echocardiaccycles, and further by classifying(quantifying) the echocardiogram asone of a normal echocardiogram or an abnormal echocardiogram based on aconvolutional neural analysis of the echocardiogram diagnostic volume.

A fourth embodiment of the present disclosure is the convolutionalneural diagnostic echo method including a lead system generatingelectrocardiogram data, and an electrocardiogram controller controllinga generation of an electrocardiogram derived from electrocardiogramdata. The electrocardiogram includes a temporal sequence ofelectrocardiogram waves.

The convolutional neural cardiac diagnostic method further includes thecardiac diagnostic controller controlling the diagnosis of theechocardiogram by generating an electrocardiogram diagnostic volumeincluding a periodic stacking of the temporal sequence of electrocardiacwaves and by further classifying(quantifying) the echocardiogram as oneof a normal echocardiogram or an abnormal echocardiogram based on aconvolutional neural analysis of both the echocardiogram diagnosticvolume and the electrocardiogram diagnostic volume.

Various embodiments described herein relate to a convolutional neuralcardiac diagnostic system, including one or more of the following: anultrasound device structurally configured to generate echocardiogramdata; an echocardiogram controller structurally configured to control ageneration of an echocardiogram derived from a generation of theechocardiogram data by the ultrasound device, the echocardiogramincluding a temporal sequence of echocardiac cycles; and anechocardiogram diagnostic controller structurally configured to controla diagnosis of the echocardiogram, wherein the echocardiogram diagnosticcontroller includes: a diagnostic periodic volume generator structurallyconfigured to generate an echocardiogram diagnostic volume derived froma generation of the echocardiogram by the echocardiogram controller, theechocardiogram diagnostic volume including a periodic stacking of thetemporal sequence of echocardiac cycles; and a diagnostic convolutionalneural network structurally configured to classify the echocardiogram asone of a normal echocardiogram or an abnormal echocardiogram based on aconvolutional neural analysis of the echocardiogram diagnostic volume asgenerated by the diagnostic periodic volume generator.

Various embodiments described herein relate to a convolutional neuralcardiac diagnostic system, including one or more of the following: amedical imaging modality structurally configured to generate cardiacimaging data; a cardiogram controller structurally configured to controla generation of a cardiogram derived from a generation of the cardiacimaging data by the imaging modality, the cardiogram including atemporal sequence of cardiac cycles; and a cardiogram diagnosticcontroller structurally configured to control a diagnosis of the acardiogram, wherein the cardiogram diagnostic controller includes: adiagnostic periodic volume generator structurally configured to generatea cardiogram diagnostic volume derived from a generation of thecardiogram by the cardiogram controller, the cardiogram diagnosticvolume including a periodic stacking of the temporal sequence of cardiaccycles; and a diagnostic convolutional neural network structurallyconfigured to classify the cardiogram as one of a normal cardiogram oran abnormal cardiogram based on a convolutional neural analysis of thecardiogram diagnostic volume as generated by the diagnostic periodicvolume generator.

Various embodiments described herein relate to a convolutional neuralcardiac diagnostic method, one or more of the following: an ultrasounddevice generating echocardiogram data; an echocardiogram controllercontrolling a generation of an echocardiogram derived from thegeneration of the echocardiogram data by the ultrasound device, theechocardiogram including a temporal sequence of echocardiac cycles; andan echocardiogram diagnostic controller controlling a diagnosis of theechocardiogram including: the echocardiogram diagnostic controllergenerating an echocardiogram diagnostic volume derived from a generationof the echocardiogram by the echocardiogram controller, theechocardiogram diagnostic volume including a periodic stacking of thetemporal sequence of echocardiac cycles; and the echocardiogramdiagnostic controller classifying the echocardiogram as one of a normalechocardiogram or an abnormal echocardiogram based on a convolutionalneural analysis of the echocardiogram diagnostic volume.

Various embodiments described herein relate to a non-transitorymachine-readable storage medium (e.g., a volatile or non-volatilememory) including instructions for execution by a processor, the mediumincluding one or more of: instructions for generating echocardiogramdata; instructions for controlling a generation of an echocardiogramderived from the generation of the echocardiogram data by the ultrasounddevice, the echocardiogram data including a temporal sequence ofechocardiac cycles; and instructions for controlling a diagnosis of theechocardiogram including: instructions for generating an echocardiogramdiagnostic volume derived from a generation of the echocardiogram by theechocardiogram controller, the echocardiogram diagnostic volumeincluding a periodic stacking of the temporal sequence of echocardiaccycles; and instructions for classifying the echocardiogram as one of anormal echocardiogram or an abnormal echocardiogram based on aconvolutional neural analysis of the echocardiogram diagnostic volume.

Various embodiments are described wherein the temporal sequence ofechocardiac cycles include one of: planar echocardiac images; andvolumetric echocardiac images

Various embodiments are described wherein the echocardiogram includes anadditional temporal sequence of echocardiac cycles; wherein thediagnostic periodic volume generator is further structurally configuredto generate an additional echocardiogram diagnostic volume including anperiodic stacking of the additional temporal sequence of echocardiaccycles; and wherein the diagnostic convolutional neural network isfurther structurally configured to classify the echocardiogram as one ofa normal echocardiogram or an abnormal echocardiogram based on aconvolutional neural analysis of both the echocardiogram diagnosticvolume and the additional echocardiogram diagnostic volume as generatedby the diagnostic periodic volume generator.

Various embodiments are described wherein the diagnostic convolutionalneural network includes a spatial-temporal based convolutional neuralnetwork.

Various embodiments are described wherein the diagnostic convolutionalneural network includes a memory recurrent network based convolutionalneural network.

Various embodiments are described wherein the diagnostic convolutionalneural network includes a multiple stream based convolutional neuralnetwork.

Various embodiments additionally include a lead system structurallyconfigured to generate electrocardiogram data; an electrocardiogramcontroller structurally configured to control a generation of anelectrocardiogram derived from a generation of the electrocardiogramdata by the lead system, the electrocardiogram including a temporalsequence of electrocardiac wave s; wherein the diagnostic periodicvolume generator is further structurally configured to generate anelectrocardiogram diagnostic volume derived from a generation of theelectrocardiogram by the electrocardiogram controller, theelectrocardiogram diagnostic volume including a periodic stacking of thetemporal sequence of electrocardiac wave s; and wherein the diagnosticconvolutional neural network is structurally configured to classify theechocardiogram as one of the normal echocardiogram or the abnormalechocardiogram based on a convolutional neural analysis of both theechocardiogram diagnostic volume and the electrocardiogram diagnosticvolume as generated by the diagnostic periodic volume generator.

For purposes of describing and claiming the various embodiments of thepresent disclosure,

(1) terms of the art including, but not limited to, “cardiogram”“echocardiogram”, “electrocardiogram”, “convolutional neural network”,“classifying”, “quantifying (synonymous with classifying)”, and “medicalimaging modality” are to be interpreted as understood in the art of thepresent disclosure and as exemplary described in the present disclosure;

(2) the term “normal” as a descriptive labeling of any type ofcardiogram in the present disclosure broadly encompasses, as would beunderstood by those of ordinary of skill in the art of the presentdisclosure and as exemplary described in the present disclosure, acardiogram exhibiting well known characteristics of a heartrepresentative of an absence of any type of unhealthy/fatalcardiovascular condition. Examples of a normal cardiogram include, butare not limited to, an echocardiogram exhibiting normal cardiac wallmotion related to any structural or functional abnormality and anelectrocardiogram exhibiting normal electrical activity;

(3) the term “abnormal” as descriptive of any type of cardiogram in thepresent disclosure broadly encompasses, as would be understood by thoseof ordinary of skill in the art of the present disclosure and asexemplary described in the present disclosure, a cardiogram exhibitingwell known characteristics of a heart representative of an absence ofany type of unhealthy/fatal cardiovascular condition. Examples of anabnormal cardiogram include, but are not limited to, an echocardiogramexhibiting abnormal cardiac wall motion related to any structural orfunctional abnormality and an electrocardiogram exhibiting abnormalelectrical activity;

(4) the term “echocardiac cycle” broadly encompasses, as would beunderstood by those of ordinary of skill in the art of the presentdisclosure and as exemplary described in the present disclosure, atemporal sequence of 2D echocardiac images over a single heartbeat, or atemporal sequence of 3D echocardiac images over a single heartbeat;

(5) the term “electrocardiac wave” broadly encompasses, as wouldunderstood by those ordinary of skill in the art of the presentdisclosure and as exemplary described in the present disclosure, anelectrocardiogram waveform over a single heartbeat;

(6) the term “convolutional neural analysis” broadly encompasses, asunderstood in the art of the present disclosure and as exemplarydescribed in the present disclosure, a classification of one or moreimage volumes based of a connection of features within the imagevolume(s). Examples of a convolutional neural analysis include, but isnot limited to, a spatial-temporal convolutional neural analysis, amultiple stream convolutional neural analysis and a memory recurrentconvolutional neural analysis;

(7) the term “periodic stacking” broadly encompasses, as exemplarydescribed in the present disclosure, an image digital stacking of atemporal sequence of echocardiac cycles whereby a last echocardiac sliceof any given echocardiac cycle is a neighbor of a first echo echocardiacslice of any succeeding echocardiac cycle or an image digital stackingof a temporal sequence of electrocardiac waves;

(8) the term “convolutional neural cardiac diagnostic system” broadlyencompasses all cardiac diagnostic systems, as known in the art of thepresent disclosure and hereinafter conceived, incorporating theprinciples of the present disclosure for implementing a deepconvolutional neural network to an echocardiogram based on a modellingof temporal changes in the echocardiogram. Examples of known cardiacdiagnostic systems include, but are not limited to, point-of-careultrasound ultralight scanners—hand held devices (e.g., Philips Lumifyand GE Vscan, portable ultrasound systems (e.g., Philips CX50 POC,Philips Sparq, GE Logiq series and GE Vivid cardiovascular series),cardiology solutions scanners (e.g., Philips EPIC 7, EPIC 5) andinterventional cardiology (e.g., Philips CX50 xMATRIX);

(9) the term “convolutional neural cardiac diagnostic method” broadlyencompasses all convolutional neural cardiac diagnostic methods, asknown in the art of the present disclosure and hereinafter conceived,incorporating the principles of the present disclosure implementing adeep convolutional neural network to an echocardiogram based on amodelling of temporal changes in the echocardiogram. A non-limitingexample of a known surface scanning method is Philips HeartModel;

(10) the term “controller” broadly encompasses all structuralconfigurations of an application specific main board or an applicationspecific integrated circuit for controlling an application of variousprinciples of the present disclosure as subsequently exemplarilydescribed herein. The structural configuration of the controller mayinclude, but is not limited to, processor(s), computer-usable/computerreadable storage medium(s), an operating system, application module(s),peripheral device controller(s), interface(s), bus(es), slot(s) andport(s). The labels “convolutional neural cardiac training”,“convolutional neural cardiac diagnostic”, “echo” and “ECG” as usedherein for the term “controller” distinguishes for identificationpurposes a particular controller from other controllers as described andclaimed herein without specifying or implying any additional limitationto the term “controller”.

(11) the term “application module” broadly encompasses a component of ancontroller consisting of an electronic circuit and/or an executableprogram (e.g., executable software and/or firmware stored onnon-transitory computer readable medium(s)) for executing a specificapplication. The labels “periodic volume generator” and “convolutionalneural network” as used herein for the term “module” distinguishes foridentification purposes a particular module from other modules asdescribed and claimed herein without specifying or implying anyadditional limitation to the term “application module”; and

(12) the terms “signal”, “data”, and “command” broadly encompasses allforms of a detectable physical quantity or impulse (e.g., voltage,current, or magnetic field strength) as understood in the art of thepresent disclosure and as exemplary described herein for communicatinginformation and/or instructions in support of applying variousprinciples of the present disclosure as subsequently described herein.Signal/data/command communication between components of the presentdisclosure may involve any communication method, as known in the art ofthe present disclosure and hereinafter conceived, including, but notlimited to, signal/data/command transmission/reception over any type ofwired or wireless medium/datalink and a reading of signal/data/commanduploaded to a computer-usable/computer readable storage medium.

The foregoing embodiments and other embodiments of the presentdisclosure as well as various features and advantages of the presentdisclosure will become further apparent from the following detaileddescription of various embodiments of the present disclosure read inconjunction with the accompanying drawings. The detailed description anddrawings are merely illustrative of the present disclosure rather thanlimiting, the scope of the present disclosure being defined by theappended claims and equivalents thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates six (6) views acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 1B illustrates a four chamber view acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 1C illustrates a two chamber view acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 1D illustrates long axis view acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 1E illustrates base view acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 1F illustrates mid view acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 1G illustrates apex view acquired in two-dimensionalechocardiography as known in the art of the present disclosure.

FIG. 2A illustrates an exemplary embodiment of a convolutional neuralcardiac training controller in accordance with the principles of thepresent disclosure.

FIG. 2B illustrates an exemplary embodiment of objective echocardiogramscale in accordance with the principles of the present disclosure.

FIG. 3A illustrates an exemplary two-dimensional (2D) echocardiac cycleand an electrocardiac wave as known in the art of the presentdisclosure.

FIG. 3B illustrates an exemplary three-dimensional (3D) echocardiaccycle as known in the art of the present disclosure.

FIG. 4A illustrates an exemplary periodic stacking of 2D echocardiaccycles in accordance with the principles of the present disclosure.

FIG. 4B illustrates a first exemplary set of echocardiogram trainingvolumes in accordance with the principles of the present disclosure.

FIG. 4C illustrates an exemplary periodic stacking of electrocardiacwaves in accordance with the principles of the present disclosure.

FIG. 4D illustrates an exemplary set of echocardiogram training volumesand of electrocardiogram training volumes in accordance with theprinciples of the present disclosure.

FIG. 4E illustrates an exemplary periodic stacking of 3D echocardiaccycles in accordance with the principles of the present disclosure.

FIG. 4F illustrates a second exemplary set of echocardiogram trainingvolumes in accordance with the principles of the present disclosure.

FIG. 5A illustrates an exemplary spatial-temporal based convolutionalneural analysis of an echocardiogram training volume in accordance withthe principles of the present disclosure.

FIG. 5B illustrates an exemplary spatial-temporal based convolutionalneural analysis of a pair of echocardiogram training volumes inaccordance with the principles of the present disclosure.

FIG. 5C illustrates an exemplary spatial-temporal based convolutionalneural analysis of an echocardiogram training volume and anelectrocardiogram training volume in accordance with the principles ofthe present disclosure.

FIG. 5D illustrates an exemplary multiple stream based convolutionalneural analysis of an echocardiogram training volume in accordance withthe principles of the present disclosure.

FIG. 5E illustrates an exemplary multiple stream based convolutionalneural analysis of a pair of echocardiogram training volumes inaccordance with the principles of the present disclosure.

FIG. 5F illustrates an exemplary multiple stream based convolutionalneural analysis of an echocardiogram training volume and anelectrocardiogram training volume in accordance with the principles ofthe present disclosure.

FIG. 5G illustrates an exemplary memory recurrent based convolutionalneural analysis of an echocardiogram training volume in accordance withthe principles of the present disclosure.

FIG. 5H illustrates an exemplary memory recurrent based convolutionalneural analysis of a pair of echocardiogram training volumes inaccordance with the principles of the present disclosure.

FIG. 5I illustrates an exemplary memory recurrent based convolutionalneural analysis of an echocardiogram training volume and anelectrocardiogram training volume in accordance with the principles ofthe present disclosure.

FIG. 6A illustrates a first exemplary embodiment of a convolutionalneural cardiac training workstation in accordance with the principles ofthe present disclosure.

FIG. 6B illustrates a second exemplary embodiment of a convolutionalneural cardiac training workstation in accordance with the principles ofthe present disclosure.

FIG. 7 illustrates an exemplary embodiment of a convolutional neuralcardiac diagnostic system in accordance with the principles of thepresent disclosure.

FIG. 8A illustrates a first exemplary embodiment of a convolutionalneural cardiac diagnostic controller in accordance with the principlesof the present disclosure.

FIG. 8B illustrates a second exemplary embodiment of a convolutionalneural cardiac diagnostic controller in accordance with the principlesof the present disclosure.

FIG. 9A illustrates an exemplary embodiment of a convolutional neuralcardiac diagnostic workstation in accordance with the principles of thepresent disclosure.

FIG. 9B illustrates an exemplary embodiment of a network pair ofconvolutional neural cardiac diagnostic workstations in accordance withthe principles of the present disclosure.

DETAILED DESCRIPTION

The principles of the present disclosure are applicable to any type ofcardiac diagnostic procedure including, but not limited to,echocardiography, cardiac CT, cardiac MRI, angiography, cardiac positronemission tomography (PET) and cardiac single photon computed emissiontomography (SPECT). To facilitate an understanding of the principles ofthe present disclosure, the various embodiments of the presentdisclosure will be described in the context of an echocardiographyapplication. From this description, those having ordinary skill in theart will appreciate how to apply the general principles of the presentdisclosure for any type of cardiac diagnostic procedure, otherdiagnostic procedure, or other image processing within or outside of theclinical realm.

In particular to echocardiography, FIG. 1 illustrates six (6) standardechocardiogram views consisting of a four chamber echocardiogram view10, a two chamber echocardiogram view 20, a long axis echocardiogramview 30, a base echocardiogram view 40, a mid-echocardiogram view 50 andan apex echocardiogram view 60.

As shown in FIG. 1B, four chamber echocardiogram view 10 illustrates anapical cap segment 11, an apical septum segment 12, an apical lateralsegment 13, a mid inferoseptum segment 14, a mid anterolateral segment15, a basal inferoseptum 16 and a basal anteroplateral segment 17.

As shown in FIG. 1C, two chamber echocardiogram view 20 illustrates anapical cap segment 21, an apical inferior segment 22, an apical anteriorsegment 23, a mid inferior segment 24, a mid anterior segment 25, abasal inferior segment 26 and a basal anterior segment 27.

As shown in FIG. 1D, long axis echocardiogram view 30 illustrates anapical cap segment 31, an apical lateral segment 32, an apical septumsegment 33, a mid inferolateral segment 34, a mid anteroseptum segment35, a basal inferolateral 36 and a basal anteroseptum segment 37.

As shown in FIG. 1E, base echocardiogram view 40 illustrates an anteriorsegment 41, an anterolateral segment 42, an inferolateral segment 43, aninferior segment 44, an inferoseptum segment 45 and an anteroseptumsegment 46.

As shown in FIG. 1F, mid echocardiogram view 50 illustrates an anteriorsegment 51, an anterolateral segment 52, an inferolateral segment 53, aninferior segment 54, an inferoseptum segment 55 and an anteroseptumsegment 56.

As shown in FIG. 1G, apex echocardiogram view 60 illustrates an anteriorsegment 61, a lateral segment 62, an inferior segment 63 and a septalsegment 64.

The embodiments of the present disclosure as applied to echocardiographyprovide for a detection and classification(quantification) ofcardiovascular disease (CVD) involving an occlusion in one or morearteries, which results in contractility of one or more of the segmentsshown in FIGS. 1B-1G. More particularly, any abnormalities in cardiacwall motion are detected and classification(quantification) on a segmentbasis by a convolutional neural analysis of one or more of theechocardiogram views shown in FIG. 1A.

To facilitate an understanding of a convolutional neural cardiactraining aspect of the embodiments of the present disclosure, thefollowing description of FIGS. 2A-6B teaches general principles of anconvolutional neural cardiac training of the present disclosure. Fromthis description, those having ordinary skill in the art will appreciatehow to apply the general principles of the present disclosure forimplementing numerous and various embodiments of convolutional neuralcardiac training of the present disclosure.

Referring to FIG. 2A, a convolutional neural cardiac training controller100 employs a training periodic volume generator 110 and a trainingconvolutional neural network 120 for training a detection andclassification(quantification) of CVD, particularly on a segment basis.For training purposes, convolutional neural cardiac training controller100 may further employ a database manager 130 and a training database140 as shown, or alternatively be in communication with database manager130 for purposes of accessing training database 140.

Training database 140 stores a set 141 of echocardiograms 142demonstrating normal cardiac wall motion (and/or any other normalcardiac function) and a set 143 of echocardiograms 144 demonstratingabnormal cardiac wall motion (and/or any other abnormal cardiacfunction). Training database 140 may further store a set ofelectrocardiograms (not shown) corresponding to normal echocardiogramset 141, and a set of electrocardiograms (not shown) corresponding toabnormal echocardiogram set 142.

In practice, echocardiograms 143 and 144 may include a temporal sequenceof 2D planar echo slices and/or a temporal sequence of 3D volume images.

As shown in FIG. 2B, echocardiograms as stored on training database 140may range on an echo scale 140 extending between an ideal normalechocardiogram 145 and a fatal abnormal echocardiogram 146 with amidline echocardiogram 147 representative of at a transitional statebetween a normal echocardiogram and an abnormal echocardiogram.

In practice, each normal echocardiograms 142 is positioned on echo scale140 between deal normal echocardiogram 145 and midline echocardiogram147 with a degree of cardiac wall motion normality, and each abnormalechocardiogram 144 is positioned on echo scale 140 between midlineechocardiogram 147 and fatal abnormal echocardiogram 146 with a degreeof cardiac wall motion abnormality.

Also in practice, set 141 of normal echocardiograms 142 and set 143 ofabnormal echocardiograms 144 may include a single segmentalechocardiogram view (FIG. 1A) or alternatively include subsets for twoor more segmental echocardiogram views (FIG. 1A).

Referring back to FIG. 2A, training periodic volume generator 110 is anapplication module structurally configured to generate one or morenormal echocardiogram training volume(s) 111 and one or more abnormalechocardiogram training volume(s) 112 in accordance with the principlesof the present disclosure.

Specifically, in practice, each normal echocardiogram 142 and eachabnormal echocardiogram 144 may include a temporal sequence ofechocardiac cycles.

For example, FIG. 3A illustrates an echocardiac cycle 150 _(EC)consisting of a temporal sequence of 2D planar echocardiac image slicesover a single heartbeat extending between a first echocardiac slice 151_(ESF) and a last echocardiac slice 151 _(ESL). Each echocardiogram 142and each abnormal echocardiogram 144 includes a temporal sequence ofechocardiac cycles 150 _(EC). Training periodic volume generator 110implement digital imaging processing technique(s) as known in the art ofthe present disclosure for a periodic stacking of the temporal sequenceof echocardiac cycles 150 _(EC) whereby a last echocardiac slice 151_(ESL) of any given echocardiac cycle 150 _(EC) is a neighbor of a firstecho echocardiac slice 151 _(ESF) of any succeeding echocardiac cycle150 _(EC).

For example, FIG. 4A illustrates a normal echocardiogram training volume111 a of the present disclosure derived from a periodic stacking of thetemporal sequence of an X number of echocardiac cycle 150 _(EC), X≥2, ofa normal echocardiogram 142 whereby a last echocardiac slice 151 _(ESL)of any given echocardiac cycle 150 _(EC) is a neighbor of a first echoechocardiac slice 151 _(ESF) of any succeeding echocardiac cycle 150_(EC).

In practice, training periodic volume generator 110 generates a normalechocardiogram training volume 111 a for one or more of theechocardiogram segmental views of a normal echocardiogram 142 wherebythe normal echocardiogram training volume 111 a may consist of a singledegree or a multiple-degree of normality of a cardiac wall motion perscale 140 (FIG. 2B). For example, FIG. 4B illustrates six (6) normalechocardiogram training volumes 111 a corresponding to the six (6)echocardiogram segmental views of FIG. 1A.

Similarly, FIG. 4A illustrates an abnormal echocardiogram trainingvolume 112 a of the present disclosure derived from a periodic stackingof the temporal sequence of an X number of echocardiac cycle 150 _(EC),X≥2, of an abnormal echocardiogram 144 whereby a last echocardiac slice151 _(ESL) of any given echocardiac cycle 150 _(EC) is a neighbor of afirst echo echocardiac slice 151 _(ESF) of any succeeding echocardiaccycle 150 _(EC).

In practice, training periodic volume generator 110 generates anabnormal echocardiogram training volume 112 a for one or more of theechocardiogram segmental views of an abnormal echocardiogram 144 wherebythe abnormal echocardiogram training volume 112 a may consist of asingle degree or a multiple-degree of normality of a cardiac wall motionper scale 140 (FIG. 2B). For example, FIG. 4B illustrates six (6)abnormal echocardiogram training volumes 112 a corresponding to the six(6) echocardiogram segmental views of FIG. 1A.

Referring back to FIG. 2A, training periodic volume generator 110 may befurther structurally configured to generate one or moreelectrocardiogram training volume(s) 113 in accordance with theprinciples of the present disclosure.

Specifically, as previously described, training database 140 may storean electrocardiogram corresponding to each normal echocardiogram 142 andeach abnormal echocardiogram 144 whereby each electrocardiogram includesa temporal sequence of ECG waves.

For example, FIG. 3 illustrates an ECG wave 160 _(CC) over a singleheartbeat. Training periodic volume generator 110 implement digitalimaging processing technique(s) as known in the art of the presentdisclosure for a periodic stacking of the temporal sequence of ECG waves160. For example, FIG. 4A illustrates an electrocardiogram trainingvolume 113 a of the present disclosure derived from a periodic stackingof the temporal sequence of an X number of ECG waves 160 _(CC), X≥2.

Referring back to FIG. 2A, in practice, training periodic volumegenerator 110 may generate an electrocardiogram training volume 113 afor each generated normal echocardiogram training volume 111 and eachgenerated abnormal volume 112. For example, FIG. 4D illustrates six (6)abnormal echocardiogram training volume 112 a for the six (6)echocardiogram segmental views of FIG. 1A with each abnormalechocardiogram training volume 112 a having a correspondingelectrocardiogram training volume 113 a.

Referring back to FIG. 2, in practice, each normal echocardiogram 142and each abnormal echocardiogram 144 as stored in training database 140may alternatively include a temporal sequence of echocardiac cycles ofthree-dimensional (3D) volumetric echocardiac images.

For example, FIG. 3A illustrates an echocardiac cycle 152 _(EC)consisting of a temporal sequence of 3D volumetric echocardiac images153 over a single heartbeat extending between a first volumetricechocardiac image 153 _(VEF) and a last volumetric echocardiac image 151_(VEL). Each echocardiogram 142 and each abnormal echocardiogram 144includes a temporal sequence of echocardiac cycles 152 _(EC). Trainingperiodic volume generator 110 implement digital imaging processingtechnique(s) as known in the art of the present disclosure for aperiodic stacking of the temporal sequence of echocardiac cycles 152_(EC) whereby a last volumetric echocardiac image 153 _(ESF) of anygiven echocardiac cycle 152 _(EC) is a neighbor of a first volumetricechocardiac image 151 _(ESF) of any succeeding echocardiac cycle 152_(EC).

For example, FIG. 4E illustrates an X number of echocardiac cycles 152_(EC), X≥2, extending over one or more beats. Training periodic volumegenerator 110 implement digital imaging processing technique(s) as knownin the art of the present disclosure for a periodic stacking of thetemporal sequence of echocardiac cycles 152 _(EC) 152 to form a normalechocardiogram training volume 111 b from a normal 3D echocardiogram 142or an abnormal echocardiogram training volume 112 b from an abnormal 3Dechocardiogram 144.

In practice, training periodic volume generator 110 generates a normalechocardiogram training volume 111 b for one or more of theechocardiogram segmental views of a normal 3 d echocardiogram 142whereby the normal echocardiogram training volume 111 b may consist of asingle degree or a multiple-degree of normality of a cardiac wall motionper scale 140 (FIG. 2B). For example, FIG. 4F illustrates six (6) normalechocardiogram training volume 111 b for the six (6) echocardiogramsegmental views of FIG. 1A.

Similarly in practice, training periodic volume generator 110 generatesan abnormal echocardiogram training volume 112 b for one or more of theechocardiogram segmental views of an abnormal 3D echocardiogram 144whereby the abnormal echocardiogram training volume 112 b may consist ofa single degree or a multiple-degree of abnormality of a cardiac wallmotion per scale 140 (FIG. 2B). For example, FIG. 4F illustrates six (6)normal echocardiogram training volume 112 a for the six (6)echocardiogram segmental views of FIG. 1A.

Also in practice, electrocardiogram training volumes 113 a (FIG. 4C) maybe co-generated with volumes 111 b/112 b as previously described in thepresent disclosure for volumes 111 a/112 b (FIG. 4A).

Referring back to FIG. 2A, training convolutional neural network (CNN)120 is an application module structurally configured for processing anormal echocardiogram training volume 111 to generate a normalechocardiogram classifier 121 indicative of a normal cardiac wallmotion, and for processing an abnormal echocardiogram training volume112 to output an abnormal echocardiogram classifier 122 indicative of anabnormal cardiac wall motion. If corresponding electrocardiograms areutilized, training CNN 120 processes volumes 111 and 113 to outputnormal echocardiogram classification 121, and training CNN 120 processesvolumes 112 and 113 to output abnormal echocardiogram classification.

In practice, training CNN 120 may execute any type of CNN known in theart of the present disclosure for delineating a connectivity patternbetween motion features of volumes 111, 112 and 113 (if applicable) thatfacilitates a classification of motion within volumes 111, 112 and 113(if applicable).

In one embodiment, training CNN 120 executes a basic spatial-temporalCNN involving a connectivity between layers via local filters, and aparameter sharing via convolutions. In the training process, the CNN islearned to recognize patterns in the echo images (and ECG) which isindicative of the cardiac abnormalities. The type of abnormality thatCNN is trained to recognize is defined during the training process byusing training cases (images and ECG signals) with the abnormalitypresent. The training can be commenced either with or without ECG signaldepending on availability of ECG data.

For example, FIG. 5A illustrates a training CNN 170 a for processing asegmental view of a normal echocardiogram training volume 111 or of anabnormal echocardiogram training volume 112 involving a 3D convolutionand subsampling stage 171 a of filters 172 and 174 for combining spatialand temporal information of volume 111 or volume 112 to establish afully connected stage 173 a of motion features 175 a for classification.In practice, a particular setup of training CNN 170 a in terms of thenumber and types of layers and kernels will be dependent upon (1) a sizeof volume 111 or of volume 112 (whichever is being processed), (2) adesired detection accuracy of CNN 170 a and (3) a type of abnormalitythat CNN 170 a is designed to classify/quantify.

By further example, FIG. 5B illustrates a training CNN 170 b forprocessing an additional segmental view of a normal echocardiogramtraining volume 111 or of an abnormal echocardiogram training volume 112involving a 3D convolution and subsampling stage 171 b of filters 176and 177 for combining spatial and temporal information of the additionalvolume 111 or volume 112 to establish a fully connected stage 173 b ofmotion features 175 a and motion features 175 b to output motionfeatures 175 c for classification. In practice, a particular setup oftraining CNN 170 b in terms of the number and types of layers andkernels will be dependent upon (1) a size of volumes 111 or of volumes112 (whichever is being processed), (2) a desired detection accuracy ofCNN 170 b and (3) a type of abnormality that CNN 170 b is designed toclassify/quantify.

By further example, FIG. 5C illustrates a training CNN 170 c foradditionally processing an electrocardiogram training volume 113involving a 3D convolution and subsampling stage 171 c of filters 178and 179 for combining spatial and temporal information of volume 113 toestablish a fully connected stage 173 c of motion features 175 a andwave features 175 d to output motion features 175 e for classification.In practice, a particular setup of training CNN 170 c in terms of thenumber and types of layers and kernels will be dependent upon (1) a sizeof volumes 111/113 or of volumes 112/113 (whichever is being processed),(2) a desired detection accuracy of CNN 170 c and (3) a type ofabnormality that CNN 170 c is designed to classify/quantify.

Referring back to FIG. 2A, in a second embodiment, training CNN 120executes a multiple stream CNN involving an execution of aspatial-temporal CNN for each echocardiogram slice of a normalechocardiogram training volume 111 or an abnormal echocardiogramtraining volume 112 (i.e., a spatial stream CNN) and an execution ofspatial-temporal CNN for a motion flow of normal echocardiogram trainingvolume 111 or abnormal echocardiogram training volume 112 (i.e., atemporal stream CNN). The multiple (dual) streams are combined by a latefusion of scores (e.g., an averaging a linear SVM, another neuralnetwork). The information from the multiple (dual) streams can also becombines by using shared convolutional kernels between differentstreams.

For example, FIG. 5D illustrates a training CNN 180 a for executing aspatial stream CNN 182 a for each eco echocardiogram slice 181 a of asegmental view of a normal echocardiogram training volume 111 or of anabnormal echocardiogram training volume 112, and for executing atemporal stream CNN 184 a for a motion flow 183 a of volume 111 or ofvolume 112. The multiple streams 182 a and 184 a are combined by a latescore fusion 186. In practice, a particular setup of training CNN 180 ain terms a complexity of spatial stream CNN 182 a and temporal streamCNN 184 a will be dependent upon (1) a size of volume 111 or of volume112 (whichever is being processed), (2) a desired detection accuracy ofCNN 180 a and (3) a type of abnormality that CNN 180 b is designed toclassify/quantify.

By further example, FIG. 5E illustrates a training CNN 180 b forexecuting a spatial stream CNN 182 b for each eco echocardiogram slice181 b of an additional segmental view of a normal echocardiogramtraining volume 111 or of an abnormal echocardiogram training volume112, and for executing a temporal stream CNN 184 b for a motion flow 183b of the additional volume 111 or of the additional volume 112. Themultiple streams 182 a and 184 a and the multiple streams 182 b and 184b are combined by a late score fusion 186. In practice, a particularsetup of training CNN 180 b in terms a complexity of spatial stream CNNs182 a and 182 b and of temporal stream CNN 184 a and 184 b will bedependent upon (1) a size of volumes 111 or of volumes 112 (whichever isbeing processed), (2) a desired detection accuracy of CNN 180 b and (3)a type of abnormality that CNN 180 b is designed to classify/quantify.

By further example, FIG. 5F illustrates a training CNN 180 c forexecuting a spatial stream CNN 182 c for each electrocardiac wave 187 ofan electrocardiogram training volume 113, and for executing a temporalstream CNN 184 b for a wave flow 188 of volume 113. The multiple streams182 a and 184 a and the multiple streams 182 c and 184 c are combined bya late score fusion 189. In practice, a particular setup of training CNN180 b in terms of a complexity of spatial stream CNNs 182 a and 182 cand of temporal stream CNN 184 a and 184 c will be dependent upon (1) asize of volumes 111/113 or of volumes 112/113 (whichever is beingprocessed), (2) a desired detection accuracy of CNN 170 c and (3) a typeof abnormality that CNN 170 c is designed to classify/quantify.

Referring back to FIG. 2A, in a third embodiment, training CNN 120executes a memory recurrent CNN involving an execution of aspatial-temporal CNN for each echocardiogram slice or a sliced 3D volumeof a normal echocardiogram training volume 111 or an abnormalechocardiogram training volume 112, a mean polling of the outputs of thespatial temporal CNNs, and an execution of a recurrent neural network(RNN) of the mean polling to obtain a scoring output.

For example, FIG. 5G illustrates a memory recurrent CNN 190 a involvingan execution of a mean polling 192 a of a spatial-temporal CNN 191 a foreach echocardiogram slice of a segmental view of a normal echocardiogramtraining volume 111 or an abnormal echocardiogram training volume 112,followed by an execution of a Long Short Term Memory (LSTM) RNN 193 aand LSTM RNN 194 a to obtain a scoring output 195 a. In practice, aparticular setup of training CNN 190 a in terms of a complexity ofspatial-temporal CNN 191 a, LSTM RNN 193 a and LSTM RNN 194 a will bedependent upon (1) a size of volume or of volume 112 (whichever is beingprocessed), (2) a desired detection accuracy of CNN 190 a and (3) a typeof abnormality that CNN 190 a is designed to classify/quantify.

By further example, FIG. 5H illustrates a memory recurrent CNN 190 binvolving an execution of a mean polling 192 b of a spatial-temporal CNN191 b for each echocardiogram slice of an additional segmental view of anormal echocardiogram training volume 111 or an abnormal echocardiogramtraining volume 112, followed by an execution of a Long Short TermMemory (LSTM) RNN 193 b and LSTM RNN 194 b to obtain a scoring output195 b. In practice, a particular setup of training CNN 190 b in terms ofa complexity of spatial-temporal CNNs 191 a and 191 b, and LSTM RNNs 193a, 193 b, 194 a and 194 b will be dependent upon (1) a size of volumes111 or of volumes 112 (whichever is being processed), (2) a desireddetection accuracy of CNN 190 b and (3) a type of abnormality that CNN190 b is designed to classify/quantify.

By further example, FIG. 5I illustrates a memory recurrent CNN 190 cinvolving an execution of a mean polling 197 of a spatial-temporal CNN196 for each electrocardiac wave of an electrocardiogram training volume113, followed by an execution of a Long Short Term Memory (LSTM) RNN 198and LSTM RNN 199 to obtain a scoring output 195 c. In practice, aparticular setup of training CNN 190 c in terms of a complexity ofspatial-temporal CNNs 191 a and 196, and LSTM RNNs 193 a, 193 b, 198 and199 will be dependent (1) a size of volumes 111/113 or of volumes112/113 (whichever is being processed), (2) a desired detection accuracyof CNN 190 c and (3) a type of abnormality that CNN 190 c is designed toclassify/quantify.

Referring back to FIG. 2A, normal echocardiogram classifier(s) 121 andabnormal echocardiogram classifier(s) 122 as generated by trainingconvolution neural network 120 are utilized by a diagnostic convolutionneural network for a real-time detection and characterization of anyabnormality of a cardiac wall motion as will be further described in thepresent disclosure.

In practice, controller 100 may be installed in a workstation,accessible over a network by a workstation or distributed across anetwork.

For example, FIG. 6A illustrates a workstation 200 employing a monitor201, an input device 202 and a computer 203 having controller 100installed therein.

By further example, FIG. 6B illustrates a workstation 200 employing amonitor 201, an input device 202 and a computer 203 having aconvolutional neural cardiac training device 101 installed therein.Device 101 employs training periodic volume generator 110 (FIG. 2A) andtraining CNN 120 (FIG. 2A) whereby database 140 (FIG. 2A) as managed bydatabase manager 130 is accessible by periodic volume generator 110 viaa network 210 of any type known in the art of the present disclosure.

Also in practice, controller 100 and device 101 may include a processor,a memory, a user interface, a network interface, and a storageinterconnected via one or more system buses.

The processor may be any hardware device, as known in the art of thepresent disclosure or hereinafter conceived, capable of executinginstructions stored in memory or storage or otherwise processing data.In a non-limiting example, the processor may include a microprocessor,field programmable gate array (FPGA), application-specific integratedcircuit (ASIC), or other similar devices.

The memory may include various memories, as known in the art of thepresent disclosure or hereinafter conceived, including, but not limitedto, L1, L2, or L3 cache or system memory. In a non-limiting example, thememory may include static random access memory (SRAM), dynamic RAM(DRAM), flash memory, read only memory (ROM), or other similar memorydevices.

The user interface may include one or more devices, as known in the artof the present disclosure or hereinafter conceived, for enablingcommunication with a user such as an administrator. In a non-limitingexample, the user interface may include a command line interface orgraphical user interface that may be presented to a remote terminal viathe network interface.

The network interface may include one or more devices, as known in theart of the present disclosure or hereinafter conceived, for enablingcommunication with other hardware devices. In an non-limiting example,the network interface may include a network interface card (NIC)configured to communicate according to the Ethernet protocol.Additionally, the network interface may implement a TCP/IP stack forcommunication according to the TCP/IP protocols. Various alternative oradditional hardware or configurations for the network interface will beapparent\

The storage may include one or more machine-readable storage media, asknown in the art of the present disclosure or hereinafter conceived,including, but not limited to, read-only memory (ROM), random-accessmemory (RAM), magnetic disk storage media, optical storage media,flash-memory devices, or similar storage media. In various non-limitingembodiments, the storage may store instructions for execution by theprocessor or data upon with the processor may operate. For example, thestorage may store a base operating system for controlling various basicoperations of the hardware. The storage may further store one or moreapplication modules in the form of executable software/firmware.Particularly, the storage stores executable software/firmware fortraining periodic volume generator 110 and training CNN 120.

To facilitate an understanding of a convolutional neural cardiacdiagnostic aspect of the embodiments of the present disclosure, thefollowing description of FIGS. 7-9B teaches general principles of anconvolutional neural cardiac diagnostic aspect of the presentdisclosure. From this description, those having ordinary skill in theart will appreciate how to apply the general principles of the presentdisclosure for implementing numerous and various embodiments ofconvolutional neural cardiac diagnostics of the present disclosure.

Referring to FIG. 7, a convolutional neural cardiac diagnostic system300 of the present disclosure employs an echocardiogram controller 310,a ECG wave controller 320, an echocardiogram diagnostic controller 330and one or more output devices 340 (e.g., a display, a printer, aspeaker and/or LED indicator(s)). In practice, controllers 310, 320 and330 may be fully or partially integrated, or segregated as shown.

Echocardiogram controller 310 is linked to and/or incorporates anynecessary hardware/software interface to an ultrasound transducer 350 aor an ultrasound probe 350 b positioned relative to a heart 91 of apatient 90 for receiving echocardiogram data to thereby generate anechocardiogram as known in the art of the present disclosure. Theechocardiogram includes a temporal sequence of echocardiac cycles 351with echocardiac cycle 351 includes a temporal sequence of 2D echoslices as shown or a 3D echo image. Echocardiogram controller 130sequentially communicates a temporal sequence of echocardiac cycles 352of echocardiogram 351 via wired and/or wireless channel(s) toechocardiogram diagnostic controller 330 as shown and to outputdevice(s) 340 for display.

ECG controller 320 is linked to and/or incorporates any necessaryhardware/software interface to a cable connector 360 for receivingelectrode signals from a lead system connected to patient 90 (e.g., astandard 12-lead system, Mason-Likar lead system as shown or a reducedlead system like the EASI lead system) to thereby generate anelectrocardiogram waveform 361 as known in the art of the presentdisclosure. Electrocardiogram waveform 361 includes a temporal sequenceof ECG waves 362 as shown. Echocardiogram controller 130 sequentiallycommunicates each ECG wave 362 of ECG waveform 361 via wired and/orwireless channel(s) to echocardiogram diagnostic controller 330 as shownand to output device(s) 340 for display.

Echocardiogram diagnostic controller 330 implement principles of thepresent disclosure for the detection and classification(quantification)of any abnormality of cardiac wall motion of heart 91 and for generatingan echocardiogram classification report 336 indicating a normal or anabnormal cardiac wall motion of heart 91. In practice report 336, may bedisplayed or printed with textual and/or graphical information by outputdevice(s) 340.

In one embodiment, as shown in FIG. 8A, echocardiogram diagnosticcontroller 330 employs a diagnostic periodic volume generator 331 a anda diagnostic convolutional neural network (CNN) 333 a.

Periodic volume generator 331 a is an application module structurallyconfigured for processing echo cardio cycles 352 to generate anechocardiogram training volume 332 in accordance with the principles ofthe present disclosure previously described for training periodic volumegenerator 110 (FIG. 2A). In practice, echocardiogram training volume 332consists of an X number of echo cardiac cycles 352, whereby X may beunlimited or have a maximum limitation of echo cardiac cycles 352involving a first in, first out implementation of echo cardiac cycles352.

The normality or the abnormality of echocardiogram training volume 332is unknown.

Diagnostic CNN 333 a therefore is an application module structurallyconfigured for processing echocardiogram training volume 332 to generatean echocardiogram classification report 336 a informative/illustrativeof a normality or an abnormality of the cardiac wall motion of heart 91.More particularly, diagnostic CNN 333 a executes a CNN whereby an outputof the CNN is compared to a training normal echocardiogram classifier334 a and an abnormal training echocardiogram classifier 335 a to detectand classify(quantify) a normality or an abnormality of the cardiac wallmotion of heart 91.

In practice, diagnostic CNN 333 a may execute any type of CNN known inthe art of the present disclosure for delineating a connectivity patternbetween motion features of echocardiogram training volume 332 thatfacilitates a classification of motion echocardiogram training volume332. For example, diagnostic CNN 333 a may execute a spatial-temporalCNN, a multiple stream CNN and/or a memory recurrent CNN as previouslydescribed in the present disclosure for training CNN 120 (FIGS. 5A-5I.

Also in practice, diagnostic CNN 333 a may implement any technique asknown in the art for use the CNN outputs to train diagnostic modelsbased on a normal echocardiogram 334 a and an abnormal echocardiogram335 a. For example, diagnostic CNN 333 a may employ a neural network,SVM networks developed/trained from outputs of CNN for normalechocardiogram classifier 334 a and an abnormal training echocardiogramclassifier 335 a

In a second embodiment, as shown in FIG. 8b , echocardiogram diagnosticcontroller 330 employs a diagnostic periodic volume generator 331 b anda diagnostic convolutional neural network (CNN) 333 b.

Periodic volume generator 331 b is an application module structurallyconfigured for additionally processing ECG waves 362 to generate anelectrocardiogram training volume 337 in accordance with the principlesof the present disclosure previously described for training periodicvolume generator 110 (FIG. 2A). In practice, electrocardiogram trainingvolume 337 consists of an X number of ECG waves 362, whereby X may beunlimited or have a maximum limitation of ECG waves 362 involving afirst in, first out implementation of ECG waves 362.

The normality or the abnormality of echocardiogram training volume 332is unknown.

Diagnostic CNN 333 b therefore is an application module structurallyconfigured for processing both echocardiogram training volume 332 andelectrocardiogram training volume 337 to generate an echocardiogramclassification report 336 b informative/illustrative of a normality oran abnormality of the cardiac wall motion of heart 91. Moreparticularly, diagnostic CNN 333 b executes a CNN whereby an output ofthe CNN is compared to a training normal echocardiogram classifier 334 band an abnormal training echocardiogram classifier 335 b to detect andclassify(quantify) a normality or an abnormality of the cardiac wallmotion of heart 91.

In practice, diagnostic CNN 333 a may execute any type of CNN known inthe art of the present disclosure for delineating a connectivity patternbetween motion features of echocardiogram training volume 332 and wavefeatures of electrocardiogram training volume 337 that facilitates aclassification of motion echocardiogram training volume 332. Forexample, diagnostic CNN 333 b may execute a spatial-temporal CNN, amultiple stream CNN and/or a memory recurrent CNN as previouslydescribed in the present disclosure for training CNN 120 (FIGS. 5A-5I).

Also in practice, diagnostic CNN 333 a may implement any technique asknown in the art for use the CNN outputs to train diagnostic modelsbased on a normal echocardiogram 334 a and an abnormal echocardiogram335 a. For example, diagnostic CNN 333 a may employ a neural network,SVM networks developed/trained from outputs of CNN for normalechocardiogram classifier 334 a and an abnormal training echocardiogramclassifier 335 a

Referring back to FIG. 7, in practice, echocardiogram controller 310,ECG controller 320 and echocardiogram diagnostic controller 330 may beinstalled in a workstation, accessible over a network by a workstationor distributed across a network.

For example, FIG. 9A illustrates a workstation 220 employing a monitor221, an input device 222 and a computer 223 having controller suite 301installed therein. Controller suite 301 includes controllers 310, 320and 330.

By further example, FIG. 9B illustrates a workstation 230 employing amonitor 231, an input device 232 and a computer 233 havingechocardiogram controller 310 and 320 installed therein, and furtherillustrates a workstation 240 employing a monitor 241, an input device242 and a computer 243 having echocardiogram diagnostic controller 330installed therein. Controllers 310, 320 and 330 communicate over anetwork 240 of any type as known in the art of the present disclosure.

Also in practice, controllers 310, 320 and 330 may include a processor,a memory, a user interface, a network interface, and a storageinterconnected via one or more system buses.

The processor may be any hardware device, as known in the art of thepresent disclosure or hereinafter conceived, capable of executinginstructions stored in memory or storage or otherwise processing data.In a non-limiting example, the processor may include a microprocessor,field programmable gate array (FPGA), application-specific integratedcircuit (ASIC), or other similar devices.

The memory may include various memories, as known in the art of thepresent disclosure or hereinafter conceived, including, but not limitedto, L1, L2, or L3 cache or system memory. In a non-limiting example, thememory may include static random access memory (SRAM), dynamic RAM(DRAM), flash memory, read only memory (ROM), or other similar memorydevices.

The user interface may include one or more devices, as known in the artof the present disclosure or hereinafter conceived, for enablingcommunication with a user such as an administrator. In a non-limitingexample, the user interface may include a command line interface orgraphical user interface that may be presented to a remote terminal viathe network interface.

The network interface may include one or more devices, as known in theart of the present disclosure or hereinafter conceived, for enablingcommunication with other hardware devices. In an non-limiting example,the network interface may include a network interface card (NIC)configured to communicate according to the Ethernet protocol.Additionally, the network interface may implement a TCP/IP stack forcommunication according to the TCP/IP protocols. Various alternative oradditional hardware or configurations for the network interface will beapparent\

The storage may include one or more machine-readable storage media, asknown in the art of the present disclosure or hereinafter conceived,including, but not limited to, read-only memory (ROM), random-accessmemory (RAM), magnetic disk storage media, optical storage media,flash-memory devices, or similar storage media. In various non-limitingembodiments, the storage may store instructions for execution by theprocessor or data upon with the processor may operate. For example, thestorage may store a base operating system for controlling various basicoperations of the hardware. The storage may further store one or moreapplication modules in the form of executable software/firmware.Particularly, for echocardiogram diagnostic controller 330, the storagestores executable software/firmware for training periodic volumegenerator 331 and training CNN 333.

As previously described in the present disclosure, the principles of thepresent disclosure are applicable to any type of cardiac diagnosticprocedure including, but not limited to, echocardiography, CT heartscans and cardiac MRI echocardiography, cardiac CT, cardiac MRI,angiography, cardiac positron emission tomography (PET) and cardiacsingle photon computed emission tomography (SPECT). Thus, while theembodiments of the present disclosure were described in the context ofan echocardiography application, FIG. 9A illustrates a medical imagingmodality 400 representative of an application of any type of cardiacdiagnostic procedure for detecting and classifying(quantifying) anormality or an abnormality of a cardiogram applicable to the particularcardiac diagnostic procedure.

Specifically, examples of medical imaging modality 400 includes, but arenot limited to, an ultrasound imaging modality, a X-ray computedtomography imaging modality, a magnetic resonance imaging modality, afluoroscopic imaging modality, a position emission tomography imagingmodality and a single-photo emission computed tomography imagingmodality. Any embodiment of medical imaging modality 400 employsapplicable imaging device(s) 401 and controller(s) 402 for generatingcardiograms as known in the art of the present disclosure. Thus, thetraining and diagnostic aspects of the present disclosure are based onthe particular type of cardiac imaging. In practice, the particular typeof cardiac imaging may generate 2D planar and 3D volume images asexemplary shown herein and/or generate high dimensional imaging as knownin the art of the present disclosure.

Referring to FIGS. 1-9, those having ordinary skill in the art willappreciate numerous benefits of the present disclosure including, butnot limited to, (1) a reduction of intra-observer and inter-observervariability in interpreting echo image, (2) an allowance for a robotreal-time diagnosis of a cardiovascular disease, (3) an improvement inreader confidence and a reduction in reading time of echo image and (4)an improvement in an accuracy of cardiovascular disease diagnosis bycombining information contained in echo image with electrocardiogramwaves.

Furthermore, as one having ordinary skill in the art will appreciate inview of the teachings provided herein, features, elements, components,etc. described in the present disclosure/specification and/or depictedin the drawings of the present disclosure may be implemented in variouscombinations of electronic components/circuitry, hardware, executablesoftware and executable firmware, particularly as application modules ofa controller as described in the present disclosure, and providefunctions which may be combined in a single element or multipleelements. For example, the functions of the various features, elements,components, etc. shown/illustrated/depicted in the drawings of thepresent disclosure can be provided through the use of dedicated hardwareas well as hardware capable of executing software in association withappropriate software. When provided by a processor, the functions can beprovided by a single dedicated processor, by a single shared processor,or by a plurality of individual processors, some of which can be sharedand/or multiplexed. Moreover, explicit use of the term “processor”should not be construed to refer exclusively to hardware capable ofexecuting software, and can implicitly include, without limitation,digital signal processor (“DSP”) hardware, memory (e.g., read onlymemory (“ROM”) for storing software, random access memory (“RAM”),non-volatile storage, etc.) and virtually any means and/or machine(including hardware, software, firmware, circuitry, combinationsthereof, etc.) which is capable of (and/or configurable) to performand/or control a process.

Moreover, all statements herein reciting principles, aspects, andembodiments, as well as specific examples thereof, are intended toencompass both structural and functional equivalents thereof.Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture (e.g., any elements developed that can perform the same orsubstantially similar function, regardless of structure). Thus, forexample, it will be appreciated by one having ordinary skill in the artin view of the teachings provided herein that any block diagramspresented herein can represent conceptual views of illustrative systemcomponents and/or circuitry embodying the principles described herein.Similarly, one having ordinary skill in the art should appreciate inview of the teachings provided herein that any flow charts, flowdiagrams and the like can represent various processes which can besubstantially represented in computer readable storage media and soexecuted by a computer, processor or other device with processingcapabilities, whether or not such computer or processor is explicitlyshown.

Furthermore, exemplary embodiments of the present disclosure can takethe form of a computer program product or application module accessiblefrom a computer-usable and/or computer-readable storage medium providingprogram code and/or instructions for use by or in connection with, e.g.,a computer or any instruction execution system. In accordance with thepresent disclosure, a computer-usable or computer readable storagemedium can be any apparatus that can, e.g., include, store, communicate,propagate or transport the program for use by or in connection with theinstruction execution system, apparatus or device. Such exemplary mediumcan be, e.g., an electronic, magnetic, optical, electromagnetic,infrared or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include,e.g., a semiconductor or solid state memory, magnetic tape, a removablecomputer diskette, a random access memory (RAM), a read-only memory(ROM), flash (drive), a rigid magnetic disk and an optical disk. Currentexamples of optical disks include compact disk—read only memory(CD-ROM), compact disk—read/write (CD-R/W) and DVD. Further, it shouldbe understood that any new computer-readable medium which may hereafterbe developed should also be considered as computer-readable medium asmay be used or referred to in accordance with exemplary embodiments ofthe present disclosure and disclosure.

Having described example embodiments of various systems, controllers andmethods for convolutional deep learning analysis of temporal diagnosticecho images, (which embodiments are intended to be illustrative and notlimiting), it is noted that modifications and variations can be made bypersons having ordinary skill in the art in light of the teachingsprovided herein, including the drawings of the present disclosure. It istherefore to be understood that changes can be made in/to the exampleembodiments of the present disclosure which are within the scope of theembodiments disclosed herein.

Moreover, it is contemplated that corresponding and/or related systemsincorporating and/or implementing the device or such as may beused/implemented in a device in accordance with the present disclosureare also contemplated and considered to be within the scope of thepresent disclosure. Further, corresponding and/or related method formanufacturing and/or using a device and/or system in accordance with thepresent disclosure are also contemplated and considered to be within thescope of the present disclosure.

The invention claimed is:
 1. A convolutional neural cardiac diagnostic system, comprising: an echocardiogram diagnostic controller structurally configured to control a diagnosis of an echocardiogram including a temporal sequence of echocardiac cycles, wherein the echocardiogram diagnostic controller includes: a diagnostic periodic volume generator structurally configured to generate an echocardiogram diagnostic volume derived from the echocardiogram, the echocardiogram diagnostic volume including a periodic stacking of the temporal sequence of echocardiac cycles; and a diagnostic convolutional neural network structurally configured to classify the echocardiogram as one of a normal echocardiogram or an abnormal echocardiogram based on a convolutional neural analysis of the echocardiogram diagnostic volume as generated by the diagnostic periodic volume generator.
 2. The convolutional neural cardiac diagnostic system of claim 1, further comprising: an ultrasound device structurally configured to generate echocardiogram data; and an echocardiogram controller structurally configured to control a generation of the echocardiogram derived from a generation of the echocardiogram data by the ultrasound device, the echocardiogram including a temporal sequence of echocardiac cycles.
 3. The convolutional neural cardiac diagnostic system of claim 1, wherein the temporal sequence of echocardiac cycles include one of: planar echocardiac images; and volumetric echocardiac images.
 4. The convolutional neural cardiac diagnostic system of claim 1, wherein the echocardiogram includes an additional temporal sequence of echocardiac cycles; wherein the diagnostic periodic volume generator is further structurally configured to generate an additional echocardiogram diagnostic volume including an periodic stacking of the additional temporal sequence of echocardiac cycles; and wherein the diagnostic convolutional neural network is further structurally configured to classify the echocardiogram as one of a normal echocardiogram or an abnormal echocardiogram based on a convolutional neural analysis of both the echocardiogram diagnostic volume and the additional echocardiogram diagnostic volume as generated by the diagnostic periodic volume generator.
 5. The convolutional neural cardiac diagnostic system of claim 1, wherein the diagnostic convolutional neural network includes at least one of a spatial-temporal based convolutional neural network, a multiple stream based convolutional neural network, and a memory recurrent network based convolutional neural network.
 6. The convolutional neural cardiac diagnostic system of claim 1, wherein the diagnostic periodic volume generator is further structurally configured to generate an electrocardiogram diagnostic volume derived from an electrocardiogram the temporal sequence of electrocardiac waves, the electrocardiogram diagnostic volume including a periodic stacking of the temporal sequence of electrocardiac waves; and wherein the diagnostic convolutional neural network is structurally configured to classify the echocardiogram as one of the normal echocardiogram or the abnormal echocardiogram based on a convolutional neural analysis of both the echocardiogram diagnostic volume and the electrocardiogram diagnostic volume as generated by the diagnostic periodic volume generator.
 7. The convolutional neural cardiac diagnostic system of claim 6, further comprising: a lead system structurally configured to generate electrocardiogram data; and an electrocardiogram controller structurally configured to control a generation of the electrocardiogram derived from a generation of the electrocardiogram data by the lead system.
 8. A convolutional neural cardiac diagnostic system, comprising: a cardiogram diagnostic controller structurally configured to control a diagnosis of a cardiogram including a temporal sequence of cardiac cycles, wherein the cardiogram diagnostic controller includes: a diagnostic periodic volume generator structurally configured to generate a cardiogram diagnostic volume derived from a generation of the cardiogram by the cardiogram controller, the cardiogram diagnostic volume including a periodic stacking of the temporal sequence of cardiac cycles; and a diagnostic convolutional neural network structurally configured to classify the cardiogram as one of a normal cardiogram or an abnormal cardiogram based on a convolutional neural analysis of the cardiogram diagnostic volume as generated by the diagnostic periodic volume generator.
 9. The convolutional neural cardiac diagnostic system of claim 8, further comprising: a medical imaging modality structurally configured to generate cardiac imaging data; and a cardiogram controller structurally configured to control a generation of the cardiogram derived from a generation of the cardiac imaging data by the imaging modality.
 10. The convolutional neural cardiac diagnostic system of claim 9, wherein the medical imaging modality includes at least one of an ultrasound imaging device, a X-ray computed tomography imaging device, a magnetic resonance imaging device, a fluoroscopic imaging device, a position emission tomography imaging device and a single-photo emission computed tomography imaging device.
 11. The convolutional neural cardiac diagnostic system of claim 8, wherein the temporal sequence of cardiac cycles are one of: planar cardiac images; volumetric cardiac images; and high dimensional cardiac images.
 12. The convolutional neural cardiac diagnostic system of claim 8, wherein the cardiogram includes an additional temporal sequence of cardiac cycles; wherein the diagnostic periodic volume generator is further structurally configured to generate an additional cardiogram diagnostic volume including an periodic stacking of the additional temporal sequence of cardiac cycles; and wherein the diagnostic convolutional neural network is further structurally configured to classify the cardiogram as one of a normal cardiogram or an abnormal cardiogram based on a convolutional neural analysis of both the cardiogram diagnostic volume and the additional cardiogram diagnostic volume as generated by the diagnostic periodic volume generator.
 13. The convolutional neural cardiac diagnostic system of claim 8, wherein the diagnostic convolutional neural network includes at least one of a spatial-temporal based convolutional neural network, a multiple stream based convolutional neural network, and a memory recurrent network based convolutional neural network.
 14. The convolutional neural cardiac diagnostic system of claim 8, wherein the diagnostic periodic volume generator is further structurally configured to generate an electrocardiogram diagnostic volume derived from an electrocardiogram including a temporal sequence of electrocardiac waves, the electrocardiogram diagnostic volume including a periodic stacking of the temporal sequence of electrocardiac waves; and wherein the diagnostic convolutional neural network is structurally configured to classify the cardiogram as one of the normal cardiogram or the abnormal cardiogram based on a convolutional neural analysis of both the cardiogram diagnostic volume and the electrocardiogram diagnostic volume as generated by the diagnostic periodic volume generator.
 15. The convolutional neural cardiac diagnostic system of claim 8, further comprising: a lead system structurally configured to generate electrocardiogram data; and an electrocardiogram controller structurally configured to control a generation of the electrocardiogram derived from a generation of the electrocardiogram data by the lead system.
 16. A convolutional neural cardiac diagnostic method, comprising: an echocardiogram diagnostic controller controlling a diagnosis of an echocardiogram temporal sequence of echocardiac cycles, the diagnosis including: the echocardiogram diagnostic controller generating an echocardiogram diagnostic volume derived from the echocardiogram, the echocardiogram diagnostic volume including a periodic stacking of the temporal sequence of echocardiac cycles; and the echocardiogram diagnostic controller classifying the echocardiogram as one of a normal echocardiogram or an abnormal echocardiogram based on a convolutional neural analysis of the echocardiogram diagnostic volume.
 17. The convolutional neural cardiac diagnostic method of claim 16, wherein the echocardiogram includes an additional temporal sequence of echocardiac cycles; wherein the echocardiogram diagnostic controller further generates an additional echocardiogram diagnostic volume including an periodic stacking of the additional temporal sequence of echocardiac cycles; and wherein the echocardiogram diagnostic controller classifies the echocardiogram as one of a normal echocardiogram or an abnormal echocardiogram based on a convolutional neural analysis of both the echocardiogram diagnostic volume and the additional echocardiogram diagnostic volume.
 18. The convolutional neural cardiac diagnostic method of claim 16, further comprising: an ultrasound device generating echocardiogram data; and an echocardiogram controller controlling a generation of the echocardiogram derived from the generation of the echocardiogram data by the ultrasound device.
 19. The convolutional neural cardiac diagnostic method of claim 16, further comprising: the diagnosis including: the echocardiogram diagnostic controller generating an electrocardiogram diagnostic volume derived from an electrocardiogram including a temporal sequence of electrocardiac waves, the electrocardiogram diagnostic volume including a periodic stacking of the temporal sequence of electrocardiac waves; and the echocardiogram diagnostic controller classifying the echocardiogram as one of the normal echocardiogram or the abnormal echocardiogram responsive to a convolutional analysis of both the echocardiogram diagnostic volume and the electrocardiogram diagnostic volume.
 20. The convolutional neural cardiac diagnostic method of claim 19, further comprising: a lead system generating electrocardiogram data; and an electrocardiogram controller controlling a generation of the electrocardiogram derived from a generation of the electrocardiogram data by the lead system. 